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For years, online retail has been organised around a simple assumption: the customer journey begins on a search engine, marketplace, brand website, or app. That assumption is starting to look dated.

A new Salesforce report suggests that more shoppers are now beginning with a question to an AI system, whether through ChatGPT-style assistants built on large language models, an AI tool embedded on a retailer’s website, or a conversational interface inside another platform.

According to the fourth edition of Salesforce’s State of Commerce report, the use of agentic search as the first step in the shopping journey grew 200 per cent year on year.

The findings are based on a survey of 3,450 commerce professionals across 20 countries and 13 industries, including 100 respondents from Singapore. Salesforce also drew on a consumer survey of 4,689 shoppers and behavioural data from more than 1.5 billion shoppers across 37 countries.

Also Read: Pinterest and Shopee link up to bring creator-led shopping to Indonesia

For Southeast Asian retailers, the shift is not just another channel to add to the marketing mix. It raises a more fundamental question: if customers no longer start on a brand’s own website, how does a business make sure its products, prices, inventory and brand information are accurately represented wherever discovery happens?

Discovery is moving away from owned channels

Salesforce’s behavioural data points to a sharp change in how shoppers find products. Traffic referred from AI chats grew between 150 per cent and 428 per cent year on year in every quarter measured, while overall traffic grew only in the single to low double digits.

Consumer behaviour appears to be moving in the same direction. Between August 2025 and May 2026, the rate of shoppers discovering products through brand-owned properties fell 7 per cent, while traditional search fell 15 per cent. Over the same period, the rate of consumers choosing newer discovery channels, including AI assistants, social media AI and delivery apps, grew 38 per cent.

This matters in Southeast Asia because commerce is already highly fragmented. A shopper in Singapore, Jakarta, Manila or Bangkok may move between a marketplace, TikTok, WhatsApp, a delivery app, a physical store and a brand’s website before buying. AI adds another layer to that path. It can compress the discovery process into a single conversation, but it also means the first recommendation may happen outside the retailer’s direct control.

Singapore commerce leaders appear to understand the direction of travel. Eighty-two per cent of respondents in the city-state said large language models will be essential to product discovery within the next year. Many are already adjusting their playbooks: 42 per cent are improving product content quality, 40 per cent are optimising content for conversational queries, 39 per cent are submitting data feeds to AI search platforms, and 38 per cent are rewriting product descriptions for natural language.

In plain terms, retailers are trying to make their catalogues more understandable to machines. A conventional search page may match keywords. An AI assistant tries to interpret intent: “What should I buy for a humid climate?”, “Which running shoes work for flat feet?” or “What is a good gift under S$100?” If product data is incomplete, inconsistent or badly structured, the brand may be invisible in these conversations.

Adoption is still early, but the pressure is rising

Despite the momentum, business adoption remains uneven. Only 28 per cent of Singapore organisations surveyed said they currently use agentic AI. Of those that do not, 52 per cent plan to deploy it within the next six months.

That gap between consumer behaviour and corporate readiness is where the pressure is building. Eighty-six per cent of commerce leaders said AI is raising customer expectations, while 41 per cent said meeting those expectations is harder than ever. Implementing or expanding AI has become both their top priority for the year ahead and their top anticipated challenge.

The term “agentic AI” refers to AI systems that can take actions toward a goal, rather than simply generate text or answer questions. In commerce, that could mean helping a shopper compare products, checking availability, recommending bundles, handling returns or routing a service request. Fully autonomous purchasing remains early, but the influence of AI at the discovery and decision stage is growing quickly.

Also Read: Southeast Asia’s live commerce boom enters its harder second act

In Asia Pacific, companies that have already adopted agentic AI appear to be moving beyond pilots. Only 5 per cent of APAC adopters said they are still primarily testing use cases, while 35 per cent, the largest share, said they are scaling AI across functions and teams, from service and IT to merchandising.

Adopters reported gains in customer satisfaction, personalisation, operational efficiency and employee productivity. Those benefits are attractive in a region where retail margins can be thin and customer acquisition costs have risen across marketplaces and social platforms.

The data problem behind the AI promise

The harder part is that AI does not fix messy systems by magic. In many cases, it exposes them.

Salesforce’s Singapore findings show a commerce stack under strain. Eighty-one per cent of commerce leaders said their vendor count had grown over the past two years. Only 25 per cent said their customer data is fully unified across sales, service, marketing and commerce.

The consequences are practical. Among organisations with fragmented data, 36 per cent reported slow or ineffective responses to customer issues, 32 per cent struggled to measure the impact of commerce investments, and 40 per cent said they faced high costs maintaining disconnected systems.

Omnichannel operations remain a weak point. Just 2 per cent of Singapore multichannel organisations reported no significant failure points. The most common problems were inventory not being synchronised in real time, cited by 46 per cent, and inconsistent pricing and promotions, cited by 36 per cent.

These issues are not new, but AI makes them more visible. If an AI assistant recommends a product that is out of stock, quotes the wrong price, or gives a customer a different answer from a store associate, the experience breaks. In markets such as Southeast Asia, where consumers often compare across channels before buying, these inconsistencies can quickly lead to abandoned carts or lost trust.

Physical retail is also being pulled into the same digital loop. Salesforce found that stores remain the top holiday shopping destination for 77 per cent of consumers, but the in-store journey is increasingly online. Seventy-nine per cent of shoppers use their phones while shopping in store, and 12 per cent ask an AI assistant for purchasing advice in the aisle. Meanwhile, 86 per cent of B2C respondents said customers expect the same personalisation in store as online.

Why unified data becomes the battleground

The report suggests that companies that have made progress on data unification are already seeing returns. The most commonly cited benefits include better alignment between sales, marketing and commerce teams at 44 per cent, improved customer retention and loyalty at 42 per cent, and better AI and automation outcomes at 31 per cent.

That makes data infrastructure less of a back-office concern and more of a competitive issue. As AI becomes a new gateway to shopping, retailers will need to ensure that product information, customer context, promotions, inventory and service histories can travel across channels.

Also Read: OpenAI’s Astra aims to turn AI from chatbot into digital worker

“Customers are no longer starting their search on a brand’s website or a search bar, they’re starting it in an AI chat, a social feed, or a delivery app,” said Swatantra Kumar, Regional Vice President at Salesforce. “Companies selling online and offline need to show up wherever discovery is happening — and that only works if their data is unified enough for AI to represent their products and brand accurately.”

For Southeast Asia’s commerce players, the near-term question is not whether AI will matter. It already does. The harder question is whether their systems are ready for a world where the first shopfront a customer sees may not be a shopfront at all, but an answer generated by an AI assistant.

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Sarawak is not usually the first place that comes to mind when Southeast Asia’s artificial intelligence race is discussed. Singapore has the region’s densest cloud and startup ecosystem, Malaysia’s Johor has been drawing data centre investment because of its proximity to Singapore, and Indonesia has been positioning itself around scale, talent and natural resources.

A new agreement involving SoftBank Group, Grab Holdings and Petroleum Sarawak, better known as PETROS, suggests Sarawak wants a more central role in that conversation.

Also Read: Malaysia’s sovereign AI bet: Local context becomes the next startup moat

The Japan-based technology investment group, Singapore-headquartered superapp operator and Sarawak’s state-owned oil and gas company have signed a framework agreement to explore the development of an AI and digital infrastructure platform in the Malaysian state. The agreement was signed by SoftBank Chairman and CEO Masayoshi Son, Grab Group Chairman, CEO and Co-Founder Anthony Tan, and PETROS Group CEO Janin Girie.

The deal is still at an exploratory stage. The three parties will work on a roadmap for the phased development of the platform, rather than immediately committing to a fully defined project. But the areas being studied are broad: AI compute and services, advanced technologies, robotics, local workforce development, community engagement and wider digital infrastructure.

If it progresses, the collaboration could place Sarawak in the middle of one of Southeast Asia’s most competitive technology infrastructure markets: the race to provide the power, computing capacity, connectivity and applied services needed for AI adoption.

Why Sarawak matters

AI infrastructure is not just about chips and servers. It depends heavily on land, energy, water, connectivity, regulation and a long-term pipeline of enterprise users. That is why governments across Southeast Asia have begun treating digital infrastructure as industrial policy, not just a technology sector issue.

Sarawak brings different advantages from Malaysia’s more established digital corridors. The state has significant energy resources and has been seeking to move up the value chain beyond extractive industries. PETROS is central to that ambition through the Sarawak Gas Roadmap and its role as master developer of the Kuching Low-Carbon Hub.

Under the agreement, PETROS is expected to focus on energy supply solutions, enabling infrastructure, local engagement and participation from Sarawak’s supply chain. That role is important because AI compute is energy-hungry. Data centres and high-performance computing facilities require stable, scalable and increasingly lower-carbon power sources, especially as multinational technology companies face pressure to manage emissions linked to digital growth.

Sarawak’s Post COVID-19 Development Strategy 2030 aims to lift the state to high-income status by 2030. An AI infrastructure platform, if executed well, could support that ambition by attracting higher-value investment and creating demand for engineering, cloud, operations, cybersecurity and AI-related jobs.

The challenge will be making sure the project does not become another infrastructure story where most of the value is captured by outside vendors, while the host location is left mainly with land use, power demand and limited spillover into the local economy.

SoftBank’s AI ambitions meet Southeast Asia’s infrastructure needs

SoftBank’s role in the partnership is straightforward: it brings its global technology and AI ecosystem. The group has exposure across AI infrastructure, advanced computing, semiconductors and what it calls physical AI, a term often used to describe AI systems applied to robotics, mobility, industrial automation and real-world machines.

Also Read: Acrab’s US$130M raise signals SEA’s deeper push into AI hardware

Son has been one of the most aggressive global voices on the transformative potential of AI. SoftBank’s portfolio and strategic interests span chip designer Arm, AI companies, robotics and digital platforms. Its involvement gives the Sarawak initiative a degree of international visibility, even if the agreement remains at the roadmap stage.

For Southeast Asia, such partnerships are increasingly important. AI adoption in the region is rising, but many markets still depend heavily on cloud infrastructure hosted elsewhere. Local compute capacity matters for latency, data governance, enterprise adoption and national digital resilience. Governments also see AI infrastructure as a way to capture more of the economic value created by digital platforms and automation.

Malaysia has been particularly active. The country has attracted large data centre and cloud commitments in recent years, helped by demand from Singapore and by its own digital economy goals. Johor has become a prominent data centre location, while Kuala Lumpur remains the country’s main corporate and technology hub. Sarawak’s pitch appears to be different: energy-linked industrial development, lower-carbon infrastructure and a broader state transformation agenda.

Grab’s role: demand, data and applied AI

Grab’s inclusion makes the agreement more than a hard infrastructure play. The company brings a large Southeast Asian digital ecosystem across mobility, deliveries, financial services, merchants and consumers. It also has experience deploying AI in areas such as matching drivers and passengers, fraud detection, routing, recommendations, merchant tools and customer service.

That applied layer matters because AI infrastructure is valuable only if there are users and use cases. A compute platform without enterprise demand risks becoming underutilised capacity. Grab could help shape commercial use cases around logistics, urban services, small business digitisation, mapping, payments and customer engagement across Southeast Asia.

Also Read: SEA’s AI boom has a water problem it cannot offset away

For Sarawak, Grab’s presence also creates a link to the region’s consumer internet economy. Many AI infrastructure discussions can feel abstract, focused on chips, data centres and national strategies. Grab’s business is closer to daily economic activity: rides, food delivery, digital payments and small merchants. If the partnership extends into local workforce and community development, Grab’s networks may help bring AI tools to smaller businesses rather than just large enterprises.

At the same time, Grab’s participation should be read carefully. The announcement does not say Grab is building a data centre, committing capital expenditure, or relocating AI operations to Sarawak. Its stated contribution is its digital ecosystem, applied AI capabilities, and customer and partner networks. The practical shape of that contribution will depend on the roadmap the parties develop.

Part of Malaysia’s broader digital agenda

The framework agreement is positioned as supporting Malaysia’s digital infrastructure and connectivity push, including the Jalinan Digital Negara initiative, or JENDELA. It is also linked to national priorities under the Ekonomi MADANI framework and the Thirteenth Malaysia Plan, both of which emphasise productivity, technology, investment and higher-value economic activity.

That alignment is not incidental. Large AI infrastructure projects typically require coordination across federal agencies, state governments, utilities, land authorities, regulators and private sector operators. By tying the collaboration to national and state development plans, the parties are signalling that the project is intended to fit into a broader policy agenda rather than stand alone as a private commercial venture.

For Southeast Asia, the announcement reflects a wider pattern. Countries are trying to move from being markets for digital services to becoming infrastructure nodes for the AI economy. Singapore has talent and capital but faces land and energy constraints. Malaysia has more space and power options, but must balance rapid data centre growth with grid capacity and sustainability. Indonesia, Thailand, Vietnam and the Philippines are also competing for investment in cloud, AI and digital services.

Sarawak’s opportunity lies in finding a specific role within that regional map. It is unlikely to replace Singapore as a headquarters hub or Johor as a spillover location for near-Singapore data centre demand. But it may be able to position itself around energy-backed AI infrastructure, industrial applications and a development model tied to local participation.

Also Read: Why Malaysia’s AI Nation 2030 plan matters for B2B startups

For now, the SoftBank-Grab-PETROS agreement is a starting point, not a finished blueprint. Its importance lies in what it reveals about the next phase of Southeast Asia’s AI buildout. The region’s AI race will not be won only by companies with the best models or apps. It will also be shaped by states and cities that can provide the physical foundations — power, compute, connectivity and talent — on which those models run.

If Sarawak can turn this framework into execution, it could shift from the edge of the regional tech map to a more strategic position in Malaysia’s AI infrastructure ambitions.

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The artificial intelligence boom is no longer just a story about software models, cloud platforms, or the companies racing to build the next large language model. It is increasingly a story about where chips are made, how far they travel, and whether the supply chains behind them can withstand the next geopolitical shock.

A new FedEx report suggests semiconductor companies are preparing for a more distributed manufacturing map across Asia Pacific, as demand from AI, high-performance computing, electric vehicles, advanced communications, and data centres puts fresh pressure on chip production networks.

Also Read: AI boom shields Asia Pacific, but Moody’s warns the cushion is thinning

According to FedEx’s “From AI to EVs: How APAC semiconductors drive the global innovation map” report, 35 per cent of semiconductor companies surveyed expect to expand manufacturing into more Asia Pacific markets within the next three years. Another 25 per cent plan to increase or diversify their sourcing.

The survey was conducted among attendees at the SEMICON Taiwan trade show. It therefore reflects the views of industry participants gathered at one of the region’s most important semiconductor events, rather than the entire global chip industry. Still, the findings offer a useful snapshot of how chip companies are thinking about growth, risk, and regional diversification at a time when semiconductors have become a strategic priority for governments and businesses alike.

Nearly four in five respondents pointed to Taiwan, Southeast Asia, Greater China, and Japan as the markets likely to see the strongest demand growth. That is a significant signal for Southeast Asia, which has historically played a quieter but critical role in the global chip supply chain through assembly, packaging, testing, and logistics.

From efficiency to resilience

For years, semiconductor supply chains were built around efficiency: specialised production in the lowest-cost or most capable locations, tight inventory management, and complex cross-border flows. That model delivered scale, but recent disruptions have exposed its limits.

FedEx said 30 per cent of respondents named geopolitical disruption as the biggest challenge to growth, ahead of costs and customs complexity. In response, companies are shifting towards geographic diversification, larger inventory buffers, and supply chains designed to absorb shocks rather than simply minimise costs.

That shift matters because chips are among the most complex products in global trade. FedEx said chip components can travel more than 25,000 miles and cross over 70 borders before completion. A single chip may involve design in one market, wafer fabrication in another, equipment or materials from several countries, packaging and testing elsewhere, and final integration into a device sold globally.

This complexity has become more consequential as AI adoption accelerates. Training and running advanced AI models require vast computing power. That in turn depends on AI accelerators, graphics processing units, high-bandwidth memory, networking chips, and the data centre infrastructure that connects them. FedEx said more than 2,000 new data centres are expected worldwide between 2026 and 2035.

Also Read: Malaysia’s chip suppliers face rising pressure to prove cyber resilience

Salil Chari, FedEx’s Asia Pacific president, said the coming decade of innovation would depend on the resilience of the supply chains behind AI and digital infrastructure. The point goes beyond logistics: without reliable production and movement of advanced chips, the AI economy’s growth could run into physical bottlenecks.

Southeast Asia’s opening

The global semiconductor market is forecast to reach US$975 billion by 2026, according to the report, with Asia-Pacific accounting for roughly 58 per cent of industry revenue. China alone makes up close to 29 per cent of the global market.

Within this landscape, Southeast Asia is becoming harder to ignore.

The region does not yet rival Taiwan in advanced foundry manufacturing or South Korea in memory chips. But it has long been deeply embedded in the “back end” of chip production, the assembly, packaging, and testing stages where wafers are turned into usable semiconductor components. As chip companies look to diversify operations, these capabilities could become more strategically valuable.

Singapore, for instance, has become the world’s largest chip importer, topping US$105 billion, according to the report. That reflects its role as a regional node where components move in and out for assembly, distribution, and integration into broader electronics supply chains. The city-state also hosts semiconductor manufacturing, precision engineering, logistics, and research operations, making it one of Southeast Asia’s most mature chip ecosystems.

Vietnam is emerging quickly. FedEx said the country’s chip exports have grown at a compound annual growth rate of 32 per cent since 2006, with integrated-circuit exports reaching US$32.4 billion in 2023. Its rise is part of a wider manufacturing shift as electronics companies expand beyond China and seek alternative production bases in Asia.

Malaysia and the Philippines are not highlighted in the same figures, but they remain important players in assembly, testing, and electronics manufacturing. Malaysia in particular has built a strong position in semiconductor packaging and testing over several decades, while Penang has become one of the region’s most important electronics manufacturing clusters.

Also Read: Malaysia wants 300,000 AI jobs by 2030. Talent will decide if it gets there

For Southeast Asian governments, the opportunity is clear but not simple. Moving up the semiconductor value chainrequires specialised talent, reliable power, water infrastructure, industrial land, long-term policy stability, and deep links with global equipment, materials, and design firms. The capital requirements are high, and competition for investment is intense.

Asia Pacific remains the centre of gravity

FedEx’s report underlines how specialised the semiconductor map has become across Asia-Pacific. Taiwan leads advanced manufacturing with roughly 60 per cent of global foundry output. South Korea dominates memory chips. Japan supplies critical equipment and materials and holds about 30 per cent of the global equipment market. China is both a major materials supplier and a growing fabrication base.

This concentration has made Asia-Pacific indispensable to the global technology economy. It has also made the region a focal point for industrial policy, export controls, and national security debates.

For startups and technology companies in Southeast Asia, these shifts may feel distant, but they are increasingly relevant. AI adoption, cloud services, electric mobility, fintech infrastructure, robotics, and advanced manufacturing all depend on semiconductor availability. Chip shortages can delay product launches, raise hardware costs, and constrain growth in sectors that rely on connected devices or computing power.

AI accelerators and high-bandwidth memory chips now account for about a fifth of chip sales, FedEx said, making them the main growth drivers in the industry. That demand is unlikely to ease soon as enterprises, governments, and cloud providers build out AI capacity.

Also Read: Southeast Asia’s chip-hub ambition is colliding with its chip-smuggling problem

The larger question is whether supply chains can scale fast enough while becoming more resilient. For Asia-Pacific, the answer will shape not only the semiconductor industry but also the next phase of digital infrastructure, electric vehicles, advanced communications, and AI-led services.

Southeast Asia is unlikely to replace Taiwan, South Korea, Japan, or China in their core strengths. But as chip companies spread risk and add capacity, the region has a chance to deepen its role in one of the world’s most strategically important industries. The next three years may determine how much of that opportunity it can capture.

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Asia’s AI founders do not lack investor attention. What they lack is the expensive plumbing frontier AI demands: models, compute, technical help and patient capital. Granite Asia and Google AI Futures Fund want to bundle all of it into one programme.

The Singapore-headquartered VC firm, which manages around US$11 billion in assets and co-managed capital, will co-invest up to US$2 million per startup alongside Google, through the Granite Asia Fellows Program. Selected founders also get up to US$350,000 in Gemini and Google Cloud credits, early access to models such as Gemini, Nano Banana and Lyria, and direct support from Google DeepMind researchers and engineers.

The programme targets five areas: science and knowledge, industrial transformation, work, creativity and entertainment. Granite Asia promises founders full choice over their AI stack, a notable pledge when most startups fear lock-in to a single provider.

The bigger question is whether platform deals like this build durable companies or merely subsidise early infrastructure bills, as Microsoft, AWS, Nvidia and Google all court the same founders with credits and model access. Cloud credits are not revenue. The real test comes later: paying customers, defensible products and proof that a startup is more than a clever demo.

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INTERVIEWS AND FEATURES

DayOne’s Nasdaq filing shows who is capturing SEA’s AI value: The data centre operator’s revenue tripled to US$512M in H1 2026, yet one customer supplies 69% of sales. The region provides land, water and power while most of the AI equity upside leaves.

Jenni hits US$10M ARR selling researchers more than a chatbot: The US-based academic writing platform has six million users who have written over 15 million papers. It turned profitable after raising only a small angel round, and makes its case against general-purpose tools.

SEA engineering teams embrace AI agents, but keep them off production: Developers from Singapore to Ho Chi Minh City now use agents to plan tasks, refactor repositories and generate tests. Tech leaders still stop short of production access, where the cost of a mistake is highest.

SEA insurers must prepare for customers who send AI agents instead: A Google Cloud and CoverGo whitepaper argues early generative AI wins, from claims extraction to call-centre drafts, may leave insurers unready for a shift to customer-side agents.

When carers have dementia too, Japan turns to physical AI: The feature unpacks rōrō kaigo and ninnin kaigo, where frail or dementia-affected elderly people care for one another, and why Japan is relying on physical AI to fill gaps in eldercare.

Southeast Asia’s foodtech winners are the least glamorous players: Lab-grown shrimp and 15-minute grocery delivery grabbed headlines, but the 2022 correction, eFishery’s scandal and TaniHub’s collapse thinned the herd. The survivors tend to solve duller, harder problems.

REGIONAL

Endeavor Catalyst closes oversubscribed US$320M fifth fund: The fund, announced from Singapore, lifts assets under management past US$850M and lands as startup markets outside Silicon Valley begin to stir after several difficult years for venture-backed companies.

Khazanah’s Dana Impak mobilises US$635M for 130 Malaysian firms: Every dollar Khazanah deployed drew 48 cents of external capital. Its Jelawang Capital arm holds up to US$244M to grow Malaysia’s fund manager pool, while mid-tier firms and semiconductors form the other priorities.

Manus parent raises over US$500M after China forces Meta deal unwind: Boyu Capital and IDG Capital co-led the round, with Tencent, HSG and ZhenFund returning. The Singapore-headquartered agent developer disclosed no valuation or plans for the money after Beijing blocked Meta’s US$2B-plus acquisition.

MAS gives Singapore financial firms a year to meet AI risk rules: The principles-based guidelines cover banks, insurers and payment firms as AI moves from back-office experiments into systems that make customer, risk and execution decisions in the region’s leading financial hub.

Revolut commits nearly US$274M to Singapore, targets 300-plus staff: The UK fintech signed a five-year Collyer Quay lease, opening in early 2027, and will hire across engineering, data and AI. It still lacks a Singapore banking licence, operating as a Major Payment Institution.

StarHub to acquire MyRepublic Mobile as Singapore telcos consolidate: The deal completes a multi-year journey that began with StarHub’s 2021 investment in MyRepublic Broadband, and follows reports of StarHub talks to buy M1 from Keppel. Subscribers move onto StarHub’s network without disruption.

EDBI backs Universal Quantum’s US$100M+ round and Singapore R&D hub: The British trapped-ion computing firm’s Series A, co-led by DCVC and Firgun Ventures, will fund its first R&D centre outside Europe. It is the largest Series A by a UK quantum company.

MDI Ventures narrows SEA thesis to AI and digital asset infrastructure: TelkomGroup’s corporate VC arm will prioritise enterprise AI, AI infrastructure, governance software, real-world asset tokenisation and custody, and regulated digital asset rails, citing a shift in the region’s technology cycle.

EnterpriseSG puts US$94M into innovation centres, cuts 27 to 10: The five-year plan simplifies SME support and absorbs technology-matching services from Innovation Partner for Impact, which winds down in March 2027. EnterpriseSG says affected staff will be redeployed where possible.

Pine Labs launches unified payments platform in Singapore: The Indian fintech combines acquiring, acceptance, point-of-sale instalment plans and loyalty via its Fave app, with Courts, CapitaLand and FairPrice already clients. The launch follows its Philippine debut with GCash.

VinFast takes its electric buses to the US through NexMove: US deliveries start in Q1 2028 under an all-in-one service model bundling buses, charging and maintenance. About 2,000 VinFast e-buses already run in Vietnam, and Europe is next on the list.

INTERNATIONAL

US bars Microsoft, Infosys, Wipro and others from green card scheme: Washington accused the firms of fraud and will reject new and pending labour certifications. With nearly three-quarters of H-1B visas going to Indian workers, the move hits Asian talent pipelines.

OpenAI’s annualised revenue nears US$50B, not the reported US$70B: The FT says the higher figure came from investors trying to match Anthropic’s revenue methodology. The gap matters for a company that raised US$122B in March and pushed its IPO to early 2027.

New York alleges TikTok fed minors a placebo safety feature: The lawsuit claims thousands of users, including children, switched on TikTok’s Algo Refresh tool but saw no feed change. It is one of more than two dozen state cases over addictive design.

Apple reportedly co-develops LG-branded smart home devices: Bloomberg says the smart lock, thermostat, doorbell and cameras will pair with Apple’s upcoming hub, expected on 13 October, in a direct challenge to Amazon’s Ring line-up.

CYBERSECURITY

58% of Singapore firms hit by AI-driven phishing attacks: Yubico and Okta’s survey of 1,890 security professionals found 81% enforce multi-factor authentication, yet 41% still rely onpasswords — while 95% want human oversight before AI agents act autonomously.

Hackers breach Asos via Snowflake, then message customers in its app: The Xuanye Group reportedly impersonated a trusted contact to obtain logins, taking names, addresses and profile notes. Asos, with 17 million customers, faces extortion; Snowflake denies its own systems were breached.

Anthropic launches Cyber Mission to arm critical infrastructure defenders: The programme pairs AI models with CrowdStrike’s security tools for power, water, and transport operators, alongside a free OSS Scanner — though its unreviewed vulnerability reports risk inaccuracies and added noise.

SEMICONDUCTOR

GlobalFoundries to make AI chip interposers for TSMC in US$2B deal: Output at GlobalFoundries’ New York plant ramps from H1 2028 under a five-year pact, adding a US source for CoWoS packaging. GlobalFoundries also operates a fab in Singapore.

TSMC posts record quarterly revenue on AI chip demand surge: The world’s largest contract chipmaker’s Q3 revenue hit $46.71 billion, up 50% year-on-year and ahead of forecasts, as AI-driven demand from clients like Nvidia and Apple continues to outpace expectations.

A third of chipmakers plan APAC expansion as AI demand surges: FedEx: Singapore is now the world’s largest chip importer at over US$105B, while Vietnam’s chip exports have grown 32% a year since 2006. Respondents ranked geopolitical disruption the biggest growth risk.

AI

Singtel’s RE:AI, SIT-NVIDIA centre tackle AI deployment gap: Running a pilot is no longer the hard part for companies; turning pilots into reliable operations is. The partnership with the Singapore Institute of Technology targets that step.

Websites are shutting the door on personal AI shopping agents: Amazon blocked Meta’s Muse, while anti-bot checks trip up others. Meta, Walmart, Stripe and others are building an open agent standard to separate legitimate agents from malicious bots.

ChatGPT adds interactive visuals with GPT-6’s Intelligent UI: Answers can now include calculators, editable graphs and charts, which users can dial back. The feature rolls out globally, including to the free and lower-cost Go tiers.

THOUGHT LEADERSHIP

ASEAN’s startup ecosystem is entering its accountability phase: Citing post-Enron America and post-Luckin Coffee China, Bobby Yulandika Putra argues every maturing ecosystem faces scrutiny, and Southeast Asia’s turn has arrived.

Why SEA is underbuilt in the categories behind durable companies: Grab, Sea and GoTo won by amassing users and burning capital. Chris Chen argues the consumer playbook leaves gaps in the categories that produce the region’s longest-lasting companies.

AI labs want the corporate foothold Excel once gave Microsoft: Alexander Khomenko argues frontier labs are chasing enterprise lock-in across Southeast Asia, aiming to embed themselves in company workflows as deeply as spreadsheets once did.

Operators risk scaling on ASEAN’s old trade rulebook after upgrade: For firms shipping into several ASEAN markets, the question has moved from tariffs to certificates and proof of origin. Fathhi Mohamed argues most are unprepared for the paperwork.

SEA is digitising health records; making them portable is harder: Patient portals and health wallets are the easy part. With WHO and Temasek Foundation backing a three-year initiative, Ubaid Pisuwala argues interoperability is the real challenge.

AI writes code faster, but engineers now struggle to trust it: Citing Google’s 2025 DORA report, James Kithokoi argues faster code generation has moved the bottleneck to review and verification, so teams are not shipping as fast as promised.

DePIN and RWA alliances find fertile ground in Southeast Asia: Astrid Dang argues decentralised infrastructure networks and tokenised real-world assets could address high infrastructure costs, thin banking reach and volatile local currencies across the region.

AI could make concierge medicine the new front door to healthcare: Concierge practices cap panels at 400 to 600 patients, against 2,000-plus for a typical primary-care doctor. John Millar argues AI could scale scarce physician attention to far more people.

Europe’s trade truce with the US has not restored trust in US firms: Shawn Balakrishnan argues American companies can no longer assume they are seen as predictable and reliable commercial partners, even after the latest phase of US-European détente.

Why founders should sometimes say no to paying customers: More contracts look like progress, but Julien Ricciarelli-Bonnal argues the wrong customers can damage growth, even for early-stage firms that feel unable to turn down revenue.

Creative talent is plentiful; dependable production capacity is not: Xiaohu Hou recounts dropping a production team despite the client accepting the video, arguing that polished samples rarely prove a vendor can deliver reliably.

Are you leading with people, or leading with tech?: Vincent Tan argues leaders who ignore where scarcity now lives keep supplying crowded markets with little demand, forgetting the basics of economics.

Light liquidations and flat funding: Is Bitcoin about to break out?: Bitcoin held a tight US$85,400 to US$85,550 band on 7 October with no sustained breakout. Anndy Lian reads low liquidations and flat funding as signs of a market coiling for a move.

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The crypto market is reeling. Bitcoin trades around US$80,550 to US$81,000. Ethereum trades near US$2,410 to US$2,470. Both assets dropped sharply on October 9, 2026. An aggressive deleveraging wave and broader macro risk-off sentiment drove the decline. Bitcoin fell about 3.3 per cent to 4 per cent over the last 24 hours. It dropped from weekly highs near US$86,500. Ethereum fell about 5.7 per cent to 6 per cent over the same period. It retreated from weekly levels above US$2,700. The total crypto market capitalisation fell 2.53 per cent to US$2.76T in 24 hours.

A massive liquidation cascade was the primary driver of this move. The crypto market shows a 75 per cent correlation with the Nasdaq-100 (QQQ). That correlation signals a shared macro-driven move. This sell-off is not isolated. It reflects a broader risk-off mood across financial markets. Investors have shown little appetite for speculative positions in this environment. The crypto sector is feeling the same pressure that weighs on technology stocks and other risk assets.

The primary reason for this sell-off is a concentrated wave of long liquidations. Bitcoin led the forced closures. More than US$300M in leveraged positions disappeared. The drop below US$77,000 triggered a concentrated liquidation wave. Over 24 hours, US$303.08M in BTC long positions were forcibly closed. That figure marks a 62.97 per cent surge from the prior period. This forced selling in the largest asset spilled over and pressured the entire market.

Excessive leverage acted as fuel. It turned a routine pullback into an accelerated decline. High volume spiked 15.02 per cent overall. Derivatives volume spiked 65.95 per cent. These volume spikes confirm panic selling and position unwinding. The cascade did not spare Ethereum. The second-largest asset suffered its own wave of liquidations. The entire crypto ecosystem felt the aftershock as traders rushed to reduce exposure.

Also Read: Asian investors aren’t choosing between crypto and TradFi anymore

Traders should watch for a sustained drop in open interest and funding rates. Such a drop would signal that deleveraging is cooling. Until that happens, prices remain vulnerable to further bouts of forced selling. The current environment rewards caution. Excessive leverage built up in the system over recent weeks. That leverage created a fragile structure. When prices dipped below key thresholds, the structure collapsed.

The high volume confirms that many participants exited positions in a hurry. Panic selling often marks a short-term bottom. It can also precede further declines if support levels fail. The next few sessions will reveal whether the deleveraging has run its course. A calm derivatives market would provide the first real sign of stability. Until then, every rally attempt risks another liquidation cascade.

Secondary pressures came from sector-specific worries. Ethereum fell 8.62 per cent over 7 days. Fears that artificial intelligence could break elliptic-curve cryptography partly drove that drop. Coinbase’s cryptographer debated this issue. The debate sparked concern among holders. At the same time, overheated altcoins saw sharp profit-taking.

NEAR and ZEC corrected after extended rallies. NEAR fell 15.79 per cent. ZEC fell 10.39 per cent. Those declines followed massive 30d rallies. NEAR had gained 91.61 per cent. ZEC had gained 567.13 per cent. The sell-off was not monolithic. It combined technical profit-taking in overbought altcoins with nascent, fear-driven narratives about core blockchain security. These narratives added a new layer of uncertainty to an already fragile ecosystem.

The AI cryptography discussion matters for the long term. In the short term, it gave traders another reason to sell. The debate itself is healthy. Panic selling based on it is not. Ethereum’s 8.62 per cent 7d decline outpaced Bitcoin’s 24-hour drop, which shows altcoins carry more beta in this risk-off phase.

Also Read: Did the Fed just accidentally kick off the next crypto bull run? Or just a dead cat bounce?

The near-term outlook hinges on key technical levels. The market is testing a key Fibonacci 50 per cent retracement level at US$2.76T. The immediate path depends on whether this support holds. The next critical level is the 61.8 per cent retracement at US$2.71T. Upcoming macro cues, such as the next FOMC decision, will influence broader risk appetite. A hold above US$2.71T could lead to a period of consolidation and base-building.

A break below that level would likely trigger another leg down toward the swing low of US$2.57T. This risk is especially high if leveraged long positions rebuild too quickly and become vulnerable again. The asset class needs time to heal. It also needs a calmer macro backdrop. The correlation with the Nasdaq-100 means crypto will struggle to rally if technology stocks remain under pressure. The US$2.76T level matters as the first test. The US$2.71T level matters as the last line of defense before US$2.57T.

In my view, this downturn is a violent deleveraging event. It is not a fundamental rejection of the asset class. Excessive leverage built up across BTC and altcoins. The liquidation cascade provided a brutal reset. The AI cryptography fears, while legitimate in the long run, have amplified short-term panic. The core technology behind Bitcoin and Ethereum remains sound. The cryptographic community continues to work on quantum-resistant solutions.

The real risk is not that AI will break elliptic-curve cryptography tomorrow. The real risk is that fear over such a possibility could drive investors away from the asset class. The macro environment is the more pressing concern.

As long as the Federal Reserve maintains a hawkish stance and geopolitical tensions simmer, crypto will struggle to mount a sustained recovery. The US$2.71T level is the line in the sand. If it holds, we could see stabilisation in the coming weeks. If it breaks, prices may need to retest their July lows. The pain could extend well into the final quarter of the year.

Market Outlook: Bearish Pressure. The downturn was primarily a violent deleveraging event. Sector-specific anxieties and profit-taking amplified it. The ability to stabilise now depends on holding key technical supports and avoiding another buildup of speculative long leverage. Will the US$2.71T level hold, or is a retest of the July lows ahead? The answer will shape the direction of the crypto sector for weeks to come. For now, caution remains the order of the day.

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The crypto market woke up to a familiar tension this morning. Bitcoin trades at US$83,079.42, down 1.02 per cent over the past 24 hours, while trading volume has surged by roughly 40 per cent. That combination of falling price and rising volume rarely signals calm. It tells a story of forced exits, not quiet repositioning. The world’s largest digital asset slipped below US$84,000 overnight and tested the US$83,000 support level, a zone that has repeatedly acted as a floor since the September recovery.

The immediate trigger for this pullback stems from macro forces unrelated to blockchain technology. The Federal Reserve released minutes from its September 15 to 16 meeting on October 7, and the document carried a distinctly hawkish tone. The FOMC voted unanimously to raise the federal funds rate by 25 basis points to a target range of 3.75 per cent to 4.00 per cent, marking the first rate hike since July 2023.

More importantly, most participants assessed that another increase in the target range would likely be appropriate by year-end. Officials pointed to persistent geopolitical tensions that have pushed up crude oil and refined fuel prices, alongside a surge in artificial intelligence-related investment that has added to inflation pressures. The CME FedWatch tool now puts the probability of a December hike at 70.5 per cent, up sharply from earlier expectations.

Oil markets have amplified this pressure. Brent crude futures climbed above US$101 per barrel this week, gaining 93 cents or 0.92 per cent to reach US$101.51 by early Wednesday trading. The International Monetary Fund has warned that high energy prices could persist into 2027 even if current hostilities in the Gulf region end quickly.

Higher crude feeds directly into inflation expectations, which in turn keeps the Fed under pressure to maintain tight policy and keeps bond yields elevated. The 10-year Treasury yield has climbed above 5.3 per cent while the 30-year sits above 5.6 per cent. A stronger US dollar has accompanied this move, pushing the dollar index above 102 against major peers and tightening financial conditions across risk assets.

The liquidation data tells the human story behind these numbers. Between US$550 million and US$690 million in crypto positions were liquidated over the past 24 hours, with the overwhelming majority coming from leveraged long positions. Bitcoin had already fallen from approximately US$85,341 to US$83,790 in roughly 20 minutes during the first major liquidation wave. This mechanism matters because forced liquidations create additional selling regardless of whether holders actually want to exit.

One particularly striking detail emerged from on-chain tracking: four separate wallets opened short positions against 148.49 BTC with 40x leverage on the decentralised exchange Hyperliquid just before the rapid price drop. The cascade that followed wiped out hundreds of millions in bullish bets, with roughly US$487 million coming from long positions alone.

Also Read: Light liquidations and flat funding: Is Bitcoin about to explode?

Beneath this violent surface, something more constructive is taking place. Exchange outflows hit a 7-month high on October 5, with approximately 24,073 BTC moving from centralised trading venues into private storage or custody solutions. This marks the largest single-day net withdrawal since March 1, pushing exchange-held Bitcoin down to roughly 6.50 per cent of the total supply.

A shrinking liquid supply can support prices over time because fewer coins sit on order books ready for immediate sale. The destination of these withdrawn coins remains unconfirmed. They may reflect long-term cold storage by institutional holders, repositioning by large wallets ahead of an anticipated move, or routine withdrawals by retail holders. The data establish that a meaningful portion of the available float left trading platforms precisely when macro headwinds intensified.

The technical picture now hinges on a few critical levels. Support sits in the US$81,300 to US$83,000 range, with the lower bound representing a level that analysts at Bitfinex have identified as the point where sustained trading below would change the market structure and bring the US$77,000 region back into play. Resistance is positioned at US$84,000 to US$86,500, and Bitcoin has now failed three times to break above the US$87,000 area in recent weeks.

The next major catalyst arrives on October 14 with the release of the US CPI inflation report for September. That data point will serve as the first genuine test of whether disinflation dynamics are reasserting or stalling, and markets will build positioning accordingly in the days ahead.

Also Read: Can Bitcoin defend US$85,000 support, or will weakening bids send it toward US$83,000?

Three additional developments deserve attention as they shape the broader narrative.

First, on October 7, US government wallets transferred approximately US$470 million in Bitcoin, wrapped Bitcoin, and USDT to addresses that Arkham Intelligence identified as likely Coinbase Prime deposit addresses. These assets link back to the 2016 Bitfinex hack and to Alameda Research. The move has reignited speculation about potential government sales, though analysts note it could equally signify a change in custody or routine administrative work. A transfer of this size to an institutional venue represents a meaningful inflow to an exchange’s custody pool regardless of whether a sale follows immediately.

Second, Bitcoin closed September 2026 at US$83,556, marking a 6.4 per cent gain and its best September performance on record. This defied the historical pattern in which September averages a 2.87 per cent decline, and every positive August since 2013 has been followed by a red September.

The resilience came despite the 10-year Treasury yield climbing to its highest level since 2007 and oil trading above US$100 per barrel. Robust institutional demand drove this outperformance, with spot Bitcoin ETFs recording a 7-day inflow streak adding approximately US$6.6 billion in late September. The buyer’s identity has changed, and that shift has proven more powerful than seasonal tendencies.

Third, on-chain analytics firm Glassnode has flagged a structural shift in Bitcoin’s intraday demand pattern. The US trading-hour bid, which measures net price contribution during New York session hours from 9:30 a.m. to 4:00 p.m. Eastern Time, has flipped direction since the September breakout. This means US session buyers have become the dominant marginal force, a reversal from previous patterns where offshore sessions carried more weight.

The flip is not a single-session anomaly but a sustained change that Glassnode characterises as a structural feature of the post-breakout environment. This points to increased net buying pressure from US-based participants, potentially linked to the timing of institutional ETF flows, and signals evolving participation that could support price stability during domestic hours.

Also Read: Bitcoin jumped 1.81% to US$86,350.24. Is this a real breakout or a short squeeze?

The market now finds itself caught between two powerful forces. On one side stands institutional demand through ETF channels that has proven capable of overriding traditional macro headwinds and seasonal weakness. On the other side stands a hawkish Federal Reserve, surging oil prices, elevated Treasury yields, and a stronger $ that together create a challenging environment for non-yielding speculative assets.

The US$83,000 support level has held for now, but the volume accompanying this decline suggests that conviction remains fragile. All eyes turn to October 14 and the CPI report, which will determine whether the disinflation narrative that supported Bitcoin’s September rally can withstand the latest inflation data.

Active forecasting pools directly on the Polymarket Crypto Hub show how traders are positioning. In the main tracking pool asking what price Bitcoin will hit in October, sentiment has shifted sharply following the drop to US$83,000, with participants pricing in nearly 100 per cent certainty that Bitcoin will break below the US$85,000 threshold during October’s broader multi-week timeframe.

Short-term directional contracts, such as the Bitcoin Up or Down Daily Contracts, reflect highly contested intraday sentiment, with an uncertain 51 per cent chance of an upward close. Traders are positioning capital ahead of mid-month data releases in the Bitcoin price on the October 14 pool, which is a major focal point for bets on whether post-CPI inflation data will trigger a market recovery or deeper corrections.

My stance remains the same. Watch and see. Find a good entry point with a higher win rate. There is no need to rush to lose money.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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The client accepted the final video. We still decided not to work with the production team again.

Before the project, the team had looked like a strong choice. They sent us more than a dozen polished samples, with good image quality and a convincing visual standard.

We hired them to produce a five-minute promotional film for a tea brand. They had about one week to deliver the first cut. The finished video contained roughly 150 shots. More than 10 had clear problems: weak lip-sync, limited visual variety and choices suggesting that parts of the brief had not been fully understood.

We identified the most obvious issues, asked for replacements and spent about two additional days completing the revisions. The client gave some feedback and ultimately accepted the video.

Nothing about the final delivery was disastrous. The problem was what it had taken to get there.

That experience changed how I read a portfolio, especially in AI-assisted production. A portfolio can show what a team has made. It usually cannot show how much judgement someone else had to supply before the work became usable.

A portfolio shows the output, not the process

Commercial portfolios are made from selected outputs. Commercial projects are made from repeated decisions. A team can refine a small number of images or short sequences until they look exceptional. Those samples may genuinely demonstrate strong creative ability.

A five-minute commercial project asks for something else. Can quality survive across 150 connected shots? Can characters, products and environments remain consistent? Does the team understand why the client requested a change rather than simply follow the literal wording? Does someone notice a weak shot before the client does?

AI is widening access to production capability. In the creator community I work with, many people can now produce an attractive frame or polished short sequence with current image and video tools. Far fewer are people I would trust with an entire commercial project.

Once the work expands from a sample into dozens or hundreds of connected decisions, technical ability is only part of the job. Someone must also interpret the brief, maintain continuity, decide what is acceptable and catch failures before they move downstream. Those capabilities are much harder to see in a portfolio.

Also Read: AI won’t just replace jobs. It will redesign how companies work

How much judgement did the client supply?

Our tea project made that gap visible. The final video was acceptable, but reaching that point required our own quality control. We reviewed the first cut, identified the visible problems, decided which shots needed replacing and directed the corrections.

The schedule deserves some weight. One week was tight for a five-minute film containing around 150 shots, and we did not confirm every production stage as fully as we normally would. With more time, we would usually confirm the script, key visual assets and storyboard before reviewing a first cut. Better checkpoints would probably have prevented some of the problems.

I would not attribute every misunderstanding to the supplier. But the compressed schedule also revealed which problems the team caught itself and which ones reached us first.

The client should still make judgements about story, tone and message. Those belong with the brand. It should not routinely be the first party to discover inaccurate lip-sync, obvious continuity problems or a shot that falls well below the quality of the rest of the film.

A first cut does not need to be perfect. What I want to know is how much production judgement has to come back to the client before the work becomes usable.

Two suppliers can therefore deliver equally acceptable final videos while leaving very different amounts of work with the client. A portfolio rarely reveals that difference.

Creative supply is larger than dependable capacity

I see the same gap in the wider creator community I work with. There are several hundred people in the group. I would estimate that 10 to 20 per cent can contribute meaningfully to commercial projects. At the moment, only about 10 are people I would be comfortable asking to take a project from storyboard through image generation, video production and editing with limited intervention.

Also Read: How to turn your founder’s opinions into media-ready narratives

A strong sample may be enough for me to try someone on a first project. It usually takes repeated cooperation to learn what happens when the deadline is shorter, a revision is inconvenient or an early interpretation of the brief turns out to be wrong.

The same applies to a production company. Access to hundreds of creators may make it easier to assemble a team. It does not tell me how many projects the company can supervise consistently or how much senior judgement sits between individual creators and the client.

That is the capacity a buyer is actually purchasing.

Test the process, not another sample

If I were evaluating a new production team again, I would still look at its portfolio. I just would not ask for another polished sample if I wanted more confidence.

A small paid project would tell me more, particularly if it reproduced some of the difficult parts of real commercial work. I would rather see several connected scenes than one perfect shot. I would include an intermediate review and a revision. I would watch what questions the team asks before production, what problems it catches without prompting and how it responds when its first interpretation of the brief is incomplete.

The finished output would still matter. So would the amount of management required to get there.

Our tea video was delivered and accepted. That was enough for the project.

For the next one, I would want to know something the final video could not tell me: how much of the production judgement could stay with the supplier instead of coming back to us.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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Singapore’s financial regulator has set out how banks, insurers, payment companies and other financial institutions should govern artificial intelligence, as AI moves from back-office experiments into systems that can influence customer outcomes, risk decisions and even execution.

The Monetary Authority of Singapore (MAS) has issued its Guidelines on Artificial Intelligence Risk Management, a principles-based framework that will take effect on 7 October 2027. Financial institutions will be allowed to implement the rules in phases, with core expectations around governance and risk management due in 2027, and additional requirements to be met by 7 October 2028.

Also Read: AI governance is moving from promises to proof

The guidelines apply to all financial institutions and all forms of AI technology. MAS is not prescribing a single compliance model. Instead, it is asking firms to calibrate their controls based on how extensively they use AI, the complexity of those systems, and the potential harm if something goes wrong.

That distinction matters. A bank using AI to summarise internal documents does not carry the same risk as one deploying AI to approve loans, detect fraud, price insurance or interact with customers. MAS’s message is that financial firms can innovate, but they must know where AI sits in their operations, who is accountable for it, how it is tested, and what happens when it fails.

“AI has significant potential to improve financial services, from enhancing customer outcomes and strengthening risk management to improving productivity and enabling new products and services,” said Ho Hern Shin, Deputy Managing Director at MAS. “Realising these benefits sustainably requires financial institutions to understand and manage the risks that come with increasingly capable AI systems.”

A risk-based rulebook, not a blanket ban

The MAS guidelines follow a public consultation held in November 2025, during which respondents supported a principles-based and risk-proportionate approach. In plain terms, this means the regulator is not trying to stop financial institutions from using AI, nor is it asking every firm to build the same governance machinery regardless of size or risk.

Financial institutions may use their existing governance structures if those structures provide adequate oversight and cross-functional coordination. They do not need to create a dedicated AI committee simply to satisfy MAS. This will be welcomed by smaller firms and fintechs, which often lack the resources of large banks but still use AI in customer support, compliance, data analysis or product personalisation.

At the same time, MAS is making clear that AI cannot be treated as a side project controlled only by technology teams. Boards and senior management are expected to provide effective oversight, set clear roles and responsibilities, define risk appetite, and ensure policies and procedures are in place.

Also Read: The compliance paradox: More checks, more fraud

This reflects a broader shift in how regulators view AI. Early AI governance discussions often focused on ethical principles such as fairness, explainability and accountability. Those still matter, but the rise of generative AI and agentic AI systems (tools that can generate outputs, make decisions or take actions with greater autonomy) has made operational resilience, cyber risk, third-party dependency and model failure much more urgent.

What financial institutions must do

The MAS framework expects firms to manage AI risks at two levels: across the enterprise and at the level of individual use cases.

At the enterprise level, financial institutions will need to understand their overall AI exposure. This means identifying where AI is being used, maintaining inventories with an appropriate level of detail, and assessing which applications are material from a risk perspective.

At the use case level, firms must apply controls across the AI life cycle. These include data governance, testing, human oversight, cybersecurity, monitoring and change management. The life-cycle approach is important because AI risk does not end once a model is launched. Models can degrade over time, behave differently as data changes, or produce unexpected results when integrated into new workflows.

The guidelines also cover third-party AI, one of the most difficult issues facing financial institutions. Many firms do not build their own AI systems from scratch. They rely on cloud providers, software vendors, embedded AI features in enterprise tools, and external model providers. MAS says financial institutions remain accountable for AI used in the services they deliver, even when that AI is developed, operated or supplied by third parties.

Firms must therefore obtain sufficient assurance from providers, assess whether third-party AI is suitable for their intended use, and apply compensating controls where there are gaps. If risks cannot be brought within the institution’s risk appetite, MAS says the firm should consider limiting, suspending or replacing the third-party AI service.

That is a notable signal to the market. As banks and fintechs race to integrate AI copilots, fraud detection tools and automated customer engagement systems, vendor due diligence will become more demanding. AI procurement will no longer be only a technology or commercial decision; it will become a regulatory and risk management issue.

Why this matters for Southeast Asia’s fintech sector

Although the guidelines apply to Singapore-regulated financial institutions, they are likely to influence AI governance beyond the city-state. Singapore remains a regional base for many banks, insurers, payment firms, digital asset companies and fintech startups operating across Southeast Asia. When MAS raises supervisory expectations, regional compliance teams often take notice.

Also Read: The EU called ChatGPT a search engine. SEA’s AI startups should worry about what comes next

This is especially relevant because Southeast Asia’s financial services market is highly digital but unevenly regulated. Digital banks, e-wallets, buy-now-pay-later providers, remittance platforms and lending startups serve large underbanked populations, often using alternative data and automated decisioning to manage cost and scale. AI can improve fraud detection, credit scoring and customer service, but it can also create risks around bias, opaque decisions, data misuse and over-automation.

For startups, the immediate challenge will be documentation and discipline. Many young companies use AI tools informally across product, engineering, compliance and support functions. The MAS guidelines point towards a future in which financial startups will need a clearer inventory of AI use, stronger vendor controls, and evidence that higher-risk systems have been tested and monitored.

This could raise compliance costs, particularly for smaller fintechs. But it may also give serious players a clearer path to enterprise partnerships and regulatory trust. In financial services, the ability to demonstrate responsible AI governance could become a competitive advantage, especially when selling to banks or expanding into regulated markets.

The next frontier: agentic AI

MAS also flagged agentic AI as an area for further attention. Agentic systems can operate with more autonomy, access tools and execute tasks across software environments. In finance, that could eventually mean AI agents that help with portfolio management, compliance investigations, customer servicing, treasury operations or claims processing.

The upside is productivity. The risk is that autonomous systems may take actions that are hard to predict, explain or reverse. In regulated financial markets, small failures can cascade quickly if they affect transactions, customer decisions or market behaviour.

MAS plans to consult the financial sector in 2027 on what additional guidance on agentic AI would be useful. This suggests the current guidelines are not the final word, but a foundation on which more specific expectations may be built.

Also Read: SEA’s insurers face a new question: what happens when customers have agents?

The phased timeline gives financial institutions room to prepare. But the direction is clear: AI adoption in finance is moving from experimentation to supervision. Singapore wants firms to use the technology, but not at the expense of customer trust or financial stability.

For Southeast Asia’s financial sector, that may become the defining balance of the next few years: how to capture AI’s productivity gains while proving that automated systems can be governed as carefully as any other part of the financial infrastructure.

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For many companies, the harder part of artificial intelligence is no longer running a pilot. It is turning that pilot into something reliable enough to sit inside daily operations.

That gap is what Singtel’s RE:AI and the Singapore Institute of Technology (SIT) are trying to address through a new partnership with the SIT x NVIDIA AI Technology Centre.

The two parties have signed a memorandum of understanding to help enterprises and government agencies co-develop AI applications that can move from applied research into production, while also training more AI practitioners in Singapore.

Also Read: Why Southeast Asia cannot build sovereign AI on borrowed choices

Under the partnership, enterprises participating in Singtel Digital InfraCo’s Centre of Excellence for Applied AI with NVIDIA will work with SIT students, researchers from the SIT x NVIDIA AI Technology Centre (known as SNAIC) and the NVIDIA AI Technology Centre. The aim is to build purpose-specific AI tools for industry use cases, rather than rely only on off-the-shelf products that may not fit an organisation’s workflows, data requirements or regulatory obligations.

At the centre of the collaboration is RE:AI, Singtel Digital InfraCo’s sovereign AI cloud business. Sovereign cloud refers to infrastructure designed to meet local requirements around data residency, security, compliance and control. This is becoming more important as companies and public agencies experiment with AI models that may process sensitive commercial, operational or citizen data.

RE:AI will host an advanced AI testbed at SIT’s campus in the Punggol Digital District. The testbed will be powered by NVIDIA GPU infrastructure and AI solutions, giving enterprises, researchers and students a shared environment to build, test and validate applications before deploying them more widely.

Why the pilot-to-production gap matters

The announcement lands at a time when Southeast Asian enterprises are under pressure to show that AI can deliver real productivity gains, not just proof-of-concept demonstrations. Banks, telcos, transport operators, hospitals, retailers and government agencies are all exploring generative AI and machine learning, but many projects stall once they move beyond a small internal trial.

The reasons are familiar to enterprise technology teams. Models need to be tuned to sector-specific data. AI systems must integrate with legacy software. Outputs need to be explainable enough for regulated industries. Data cannot always leave the country or be sent to public cloud environments without additional controls. Most importantly, the organisation needs people who understand both the technology and the business problem.

Singapore has tried to position itself as a regional testbed for this next phase of AI adoption. Its National AI Strategy 2.0, launched in 2023, placed stronger emphasis on industry deployment, talent development and trusted infrastructure. The government’s Research, Innovation and Enterprise 2030 plan also identifies AI as a priority area for long-term economic competitiveness.

Also Read: Singapore firms embrace agentic AI but audit trails remain thin

This partnership fits into that wider policy direction. SIT brings an applied learning model and access to students, faculty, postgraduate talent and researchers. SNAIC, officially opened in October 2025 as a joint initiative between SIT and NVIDIA AI Technology Centre, focuses on applied research and industry collaboration. Singtel brings cloud infrastructure, enterprise relationships and its growing digital infrastructure business.

Manoj Prasanna Kumar, Chief Technology and Information Officer at Singtel Digital InfraCo, said the partnership is designed to bring together industry, academia and digital infrastructure to help enterprises move “from research to production deployment”.

A campus testbed for industry problems

The location of the AI testbed is significant. SIT’s centralised campus sits inside the Punggol Digital District, Singapore’s attempt to create a tighter link between industry, research and talent development. By placing the testbed on campus, the partners are trying to make enterprise AI work less abstract for students and researchers, and less isolated for companies.

For enterprises, this could mean access to multidisciplinary teams that can prototype and validate AI tools before they are integrated into live systems. For students, it offers exposure to real business problems rather than classroom-only AI exercises. That is especially relevant in Southeast Asia, where demand for AI talent is rising faster than the supply of practitioners who understand deployment constraints in sectors such as transport, finance, logistics and public services.

Professor Susanna Leong, Deputy President for Academic and Provost at SIT, said the collaboration will create opportunities for students and academic staff to tackle “complex business issues” and gain hands-on experience applying AI to practical problems.

The early examples cited by the partners come from SNAIC’s work with public transport operator SMRT. One project, GENESIS (Generative AI Aided Safety Investigation System), was developed to automate parts of safety reporting and investigation. Instead of manually searching through past records, staff can retrieve relevant incident histories, rulebook-based guidance and possible mitigation measures.

Another project, AiDiSA (AI-Driven Intelligent Situation Awareness System), helps analyse commuter feedback from selected sources. It classifies and routes cases based on severity and urgency, tracks public sentiment, and alerts teams to issues that may need closer attention.

Also Read: 163,000 workers, 37% training: Malaysia’s AI skills gap in focus

These are not flashy consumer AI applications, but they show where enterprise AI may gain traction first: in repetitive, data-heavy, operational tasks where faster retrieval, classification and escalation can improve decision-making.

The competitive landscape

RE:AI enters a crowded and fast-moving market. Global cloud providers such as Amazon Web Services, Microsoft Azure, Google Cloud and Oracle are all pushing enterprise AI infrastructure and tooling across Southeast Asia. Microsoft and Google, in particular, have tied their cloud strategies closely to generative AI services and productivity software used by large organisations.

At the same time, regional telecom and infrastructure players are trying to capture demand for sovereign cloud, GPU capacity and AI-ready data centres. Singtel’s advantage lies in its combination of connectivity, enterprise relationships, data centre exposure through Nxera, and its digital services arm, NCS. Its challenge will be to show that RE:AI can offer more than infrastructure: enterprises will judge the platform by whether it shortens deployment timelines, supports compliance and delivers measurable business outcomes.

NVIDIA’s role is also important. The chipmaker has become the backbone of much of the global AI infrastructure buildout, with its GPUs widely used for training and running AI models. Its participation gives the partnership technical weight, but it also reflects a broader regional reality: Southeast Asia wants to build AI applications, yet much of the underlying compute stack is still shaped by global technology suppliers.

A Singapore play with regional implications

Although the partnership is Singapore-based, its implications reach beyond the city-state. Many Southeast Asian markets face similar barriers to enterprise AI adoption: limited specialist talent, fragmented data systems, regulatory uncertainty and difficulty moving pilots into production. Singapore often acts as the regional headquarters for multinationals and a proving ground for regulated technologies, so successful deployment models can influence how companies roll out AI elsewhere in the region.

For Singtel, the partnership also reflects a broader shift in the telecom sector. Connectivity remains core, but telcos are increasingly trying to move up the stack into cloud, cybersecurity, data centres and enterprise AI. The reason is straightforward: as AI workloads grow, demand for secure infrastructure, low-latency networks and trusted deployment environments will grow with them.

Also Read: Why Southeast Asian enterprises need AI governance before scaling generative AI

The question now is whether collaborations like this can avoid becoming yet another layer of innovation theatre. Enterprises do not need more AI showcases. They need systems that work under real constraints, with clear accountability, trained users and measurable value.

If RE:AI, SIT and NVIDIA can help companies make that jump, the partnership could become a useful model for applied AI in Southeast Asia: less about building AI for its own sake, and more about embedding it into the industries that keep the region moving.

The post Singtel’s RE:AI partners SIT-NVIDIA centre to tackle enterprise AI deployment gap appeared first on e27.

Every mature startup ecosystem has eventually faced a period the venture community refers to, after the fact and usually with discomfort, as its accountability phase. The United States went through one in the early 2000s, after Enron, WorldCom, and the broader dot-com governance cleanup. China went through one in the late 2010s, when Luckin Coffee and a wave of regulatory enforcement actions reset expectations around financial controls inside high-growth tech companies. India entered one in 2022-2023. ASEAN is now entering its version.

I have spent 15 years inside Indonesian risk functions, and the structural patterns that produce accountability phases in startup ecosystems are not new to anyone who has worked in regulated finance. The governance gaps that surface during these moments are not technical failures. They are the predictable consequences of growth velocity outpacing institutional maturity.

The historical pattern

Three things tend to be true of accountability phases across mature ecosystems.

They follow rapid scale. Every ecosystem that has been through one entered the phase after a multi-year period of high-velocity capital deployment, valuation expansion, and founder-led decision-making. The governance infrastructure that should have accompanied that scale was outpaced by operational growth.

They surface in clusters. Accountability phases are rarely about a single company. They are about a cluster of disclosures, investigations, and resignations that arrive within 12 to 24 months of each other, often involving companies with overlapping investors, advisors, and audit relationships. The cluster reveals what individual cases sometimes obscure: the gaps were systemic.

They produce structural change. The ecosystems that handled their accountability phases best did not stop at prosecuting individual cases. They used the moment to install governance infrastructure (board independence requirements, audit committee standards, founder oversight mechanisms, investor diligence norms) that the previous era of growth had skipped.

Also Read: Startups keep scaling ops before they scale data — Here’s why it backfires

Why these phases happen

Three structural forces produce them predictably.

Velocity outpacing governance. Founders are trained to decide quickly. Boards staffed primarily by founder allies and friendly investors do not provide effective challenge. Audit committees in pre-IPO companies are often nominal. Internal financial controls grow more slowly than the revenue figures being reported. Each gap is individually defensible during growth. Together, they create the conditions for failures that only become visible when external pressure forces examination.

Diligence theatre. Late-stage venture investors, particularly in markets with limited public-company comparables, often run diligence processes that test surface metrics more rigorously than underlying operational reality. The same diligence playbook, repeated across deals and across investors, produces consistent blind spots.

Founder isolation. Founders of unicorn-scale companies eventually reach a point where almost everyone in their daily orbit benefits financially from the company’s continued narrative. The mechanisms that should challenge the founder’s interpretation of reality (independent directors, external auditors, internal risk officers with real authority) are often the same mechanisms that have been quietly weakened during growth.

What changed for the ecosystems that handled it well

Investor diligence standards reset publicly. The US after Enron, China after Luckin, India after 2022-2023: each ecosystem saw major institutional investors publicly upgrade their diligence frameworks. The new standards became the baseline, and companies that resisted them faced funding consequences.

Board governance norms changed. The composition, independence, and authority of pre-IPO company boards shifted meaningfully. Independent directors with real veto power became more common. Audit committees got teeth. Founder-CEO dual roles in companies above a certain valuation began carrying additional governance requirements.

Auditor scrutiny increased. The professional services firms that audited and advised during the growth phase faced their own accountability moment shortly after the founder cohort did. Standards tightened. The cost and rigour of independent verification increased.

Also Read: Startups should learn to leave bad markets faster

Lessons learned

Five principles from previous accountability phases are worth carrying into ASEAN’s current one.

The cluster will get bigger before it gets smaller. Accountability phases tend to surface 12 to 24 months of issues within a relatively short period. The ecosystem response is more durable if it treats the cluster as predictable rather than as a series of individual surprises.

Diligence framework upgrades are collective infrastructure. The fastest way to upgrade due diligence is for the largest investors to coordinate publicly on new standards. Individual firms upgrading alone get adverse selection. Coordinated upgrades change the market.

Independent directors are not luxury. Independent board seats with actual authority, not friendly investor representatives, are the single highest-leverage governance investment a unicorn-stage company can make.

Founder-CEO accountability needs explicit structure. The presumption that the founder will act in the company’s long-term interest is necessary but not sufficient. Specific mechanisms (board oversight, audit committee authority, risk officer independence) should be in place before they are needed.

Transparency is the recovery accelerant. The ecosystems that recovered fastest from their accountability phases were the ones where the institutions involved disclosed honestly and quickly. The ones that protected reputations through opacity recovered more slowly.

The macro stakes

ASEAN’s startup ecosystem has spent 15 years building. The capital deployed, the talent attracted, the regional infrastructure built, none of that is at risk from the current moment, unless the ecosystem responds to it with denial rather than with structural improvement.

The accountability phase, when handled well, is not a setback for an ecosystem. It is the moment an ecosystem grows up. The institutions that come out of it stronger will be the ones that treat the period as collective standard-setting rather than individual damage control. The question is whether ASEAN’s response will match the scale of what has been built.

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